In order to avoid additional computational overhead brought by the neural architecture search, we directly employ the conventional network pruning methods to compress the generator.
Previous work [1]} revealed that the core of network pruning searches for a suitable sub-architecture from the original network architecture instead of parameter inheritance.
Therefore, we leverage the one-shot pruning method [2]}, [3]} to obtain the appropriate lightweight generator architecture from the original generator architecture.
In our experiments, we adopt the slimming [3]} to prune the generator when the network contains the Batch Normalization (BN) layers.
We use L1-norm pruning [2]} on the contrary (without BN layers).
The detailed pruning process is described in Appendix B. Finally, we only keep the architecture of the pruned generator and train it from scratch with a selective activation discriminator proposed in Section REF .
